PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 20, 2026Journal of Economic Surveys4 citations

Theorizing Data Assets as a New Production Factor in Accounting and Finance: A Systematic Analytical Framework

View Full Paper
LZLuxiu ZhangYYYu YuanYLYunqing Li

Key Points

  • This research aims to establish data as a crucial production factor in accounting and finance, emphasizing its implications for corporate practices.
  • Synthesized literature review on data assets and data assetization.
  • Developed a unified analytical framework connecting data assetization to balance sheet recognition.
  • Examined national policies and accounting guidance in China regarding data as a production factor.
  • Analyzed compliance assessment, valuation, and financial presentation of data assets.
  • Identified rapid adoption of data assetization in service firms, with slower progress in traditional sectors.
  • Clarified the conceptual distinctions among data, data resources, and data assets.
  • Highlighted key constraints like measurement standardization and data quality impacting data assetization.

Abstract

ABSTRACT This study synthesizes the growing literature on data assets and data assetization and theorizes data as a new factor of production with direct implications for accounting and corporate finance. We clarify the conceptual distinctions among data, data resources, and data assets and integrate multiple theoretical perspectives into a unified analytical framework that links data assetization, balance sheet recognition, and corporate financing outcomes. Using China as the primary observation window, this research reviews the evolution of national policies on data as a production factor and recent accounting guidance that allows eligible data resources to be recognized as intangible assets or inventories. We then trace the full process of data assetization, from compliance assessment and valuation to financial statement presentation and subsequent financial use, including pledge‐backed lending, equity contributions valued with data, and securitization. Illustrative cases reveal a rapid adoption among data‐intensive service firms and more gradual progress in traditional industries. The study concludes by identifying the key constraints, including measurement standardization, cost allocation, data quality, and security compliance, and proposes a research agenda that supports more rigorous measurement frameworks and policy designs.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6997fa35ad1d9b11b3453475https://doi.org/10.1111/joes.70079
Ask AI
Helpful
Bookmark
Share
View Full Paper